Applied Scientist, AI

The role

The Applied Scientist, AI will turn ambiguous healthcare, product, and operational problems into machine learning models and AI systems. This role involves building baselines, designing evaluations, and partnering with ML engineering and clinical stakeholders to improve access to care.

What you will do

• Turn ambiguous healthcare, product, and operational problems into ML, AI, ranking, optimization, NLP, or LLM-based tasks
• Build and improve strong baselines using appropriate modeling approaches
• Develop models across traditional ML, deep learning, NLP, and LLM-based approaches
• Design offline and online evaluations
• Choose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes
• Run error analysis to improve model quality and product fit
• Identify label leakage, selection bias, and confounding
• Explore messy real-world data and assess label quality
• Partner with ML engineering to productionize models
• Work with clinical stakeholders to validate assumptions and review model errors

Required qualifications

• Built, evaluated, and iterated on machine learning or AI models for real-world use cases
• Turned ambiguous business, product, clinical, or operational problems into measurable modeling tasks
• Designed rigorous offline evaluations, experiments, or analyses
• Worked with messy real-world datasets
• Used statistical reasoning, experimental design, and error analysis
• Built models using Python and standard ML or AI tooling such as PyTorch, scikit-learn, NumPy, pandas, Polars, Hugging Face, or Matplotlib
• Compared modeling approaches and made pragmatic decisions
• Communicated model performance, limitations, tradeoffs, and uncertainty to stakeholders
• Partnered with engineering, product, data, operations, clinical, or domain experts
• Operated with enough engineering depth to run experiments end to end and self-serve deployments
• Used AI coding assistants such as Claude Code, Cursor, or similar tools

Nice to have

• MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative field
• Exceptional applied experience that substitutes for formal graduate training
• Depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI
• Shipped models that reached production and had measurable real-world impact
• Worked with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data
• Experience working with PHI, HIPAA-aware systems, or other sensitive regulated data
• Know when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right tool
• Experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts
• Worked in a startup or fast-moving applied environment

Benefits

• Meaningful pre-IPO equity
• Medical, dental, and vision plans 100% paid for you and your dependents
• Flexible PTO
• 10 paid holidays per year
• 401(k) with match
• 16-week parental leave policy for birthing parent
• 8 weeks for all other parents
• HSA + FSA contributions
• Life insurance
• Short and long-term disability coverage
• Free daily lunch in-office
• Annual learning stipend

Before you apply

• Recruiter Screen: Background fit, motivation, and compensation alignment
• Hiring Manager Interview: Applied science experience, modeling depth, and healthcare/product orientation
• Hands-on Technical Assessment: Practical modeling, evaluation, error analysis, and scientific judgment
• Onsite Interview: Technical case study, research or project presentation, behavioral interview, and lunch with the team
• References: Validation of performance, judgment, and working style

About the company

Sprinter Health aims to reimagine how people access care by bringing it directly to their homes. The company uses technologies to deliver care where people are, having supported more than 2 million patients across 22 states.